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A novel approach for protein structure prediction based on an estimation of distribution algorithm

  • Amir Morshedian
  • Jafar Razmara
  • Shahriar Lotfi
Methodologies and Application
  • 82 Downloads

Abstract

Protein structure prediction is one of the major challenges in structural biology and has wide potential applications in biotechnology. However, the problem is faced with a difficult optimization requirement with particularly complex energy landscapes. The current article aims to present a novel approach namely AHEDA as an evolutionary-based solution to overcome the problem. AHEDA uses the hydrophobic-polar model to develop a robust and efficient evolutionary-based algorithm for protein structure prediction. The method utilizes an integrated estimation of distribution algorithm that attempts to optimize the search process and prevent the destruction of structural blocks. It also uses a stochastic local search to improve its accuracy. Based on a comprehensive comparison with other existing methods on 24 widely used benchmarks, AHEDA was shown to generate highly accurate predictions compared to the other similar methods.

Keywords

Estimation of distribution algorithm (EDA) Protein structure prediction (PSP) HP model Protein folding Stochastic local search (SLS) 

Notes

Compliance with ethical standards

Conflict of interest

Authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

References

  1. Anfinsen C (1973) Principles that govern the folding of protein chains. Science 181(96):223–230CrossRefGoogle Scholar
  2. Babaei S, Geranmayeh A, Seyyedsalehi SA (2010) Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks. Comput Methods Programs Biomed 100(3):237–247CrossRefGoogle Scholar
  3. Bazzoli A, Tettamanzi AG (2004) A memetic algorithm for protein structure prediction in a 3D-lattice HP model. Workshops on applications of evolutionary computation. Springer, Berlin, pp 1–10Google Scholar
  4. Berger B, Leighton T (1998) Protein folding in the hydrophobic–hydrophilic (HP) model is NP-complete. J Comput Biol 5(1):27–40CrossRefGoogle Scholar
  5. Bujnicki JM (2006) Protein-structure prediction by recombination of fragments. ChemBioChem 7(1):19–27CrossRefGoogle Scholar
  6. Chen W, Ding H, Feng P, Lin H, Chou KC (2016) iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget 7(13):16895Google Scholar
  7. Crescenzi P, Goldman D, Papadimitriou C, Piccolboni A, Yannakakis M (1998) On the complexity of protein folding. J Comput Biol 5(3):423–465CrossRefzbMATHGoogle Scholar
  8. Custódio FL, Barbosa HJ, Dardenne LE (2014) A multiple minima genetic algorithm for protein structure prediction. Appl Soft Comput 15:88–99CrossRefGoogle Scholar
  9. Cutello V, Nicosia G, Pavone M, Timmis J (2007) An immune algorithm for protein structure prediction on lattice models. IEEE Trans Evol Comput 11(1):101–117CrossRefGoogle Scholar
  10. Davis IW, Baker D (2009) RosettaLigand docking with full ligand and receptor flexibility. J Mol Biol 385(2):381–392CrossRefGoogle Scholar
  11. De Araújo AFP (1999) Folding protein models with a simple hydrophobic energy function: the fundamental importance of monomer inside/outside segregation. Proc Nat Acad Sci 96(22):12482–12487CrossRefGoogle Scholar
  12. Lau KF, Dill KA (1989) A lattice statistical mechanics model of the conformational and sequence spaces of proteins. Macromolecules 22(10):3986–3997CrossRefGoogle Scholar
  13. Dill KA, Fiebig KM, Chan HS (1993) Cooperativity in protein-folding kinetics. Proc Nat Acad Sci 90(5):1942–1946CrossRefGoogle Scholar
  14. Dobson CM, Šali A, Karplus M (1998) Protein folding: a perspective from theory and experiment. Angew Chem Int Ed 37(7):868–893CrossRefGoogle Scholar
  15. Do DD (2017) A novel and efficient ant colony optimization algorithm for protein 3D structure prediction. VNU-UET technical reportGoogle Scholar
  16. Garza-Fabre M, Rodriguez-Tello E, Toscano-Pulido G (2015) Constraint-handling through multi-objective optimization: the hydrophobic-polar model for protein structure prediction. Comput Oper Res 53:128–153MathSciNetCrossRefzbMATHGoogle Scholar
  17. Guntert P (2004) Automated NMR structure calculation with cyana. Protein NMR Tech 278:353–378CrossRefGoogle Scholar
  18. Jana ND, Sil J, Das S (2017) An improved harmony search algorithm for protein structure prediction using 3D off-lattice model. International conference on harmony search algorithm. Springer, Singapore, pp 304–314CrossRefGoogle Scholar
  19. Kanj F, Mansour N, Khachfe H, Abu-Khzam F (2009) Protein structure prediction in the 3D HP model. In IEEE/ACS international conference on computer systems and applications, 2009. AICCSA 2009. IEEE. pp 732–736Google Scholar
  20. Khaji E, Karami M, Garkani-Nejad Z (2016) 3D protein structure prediction using Imperialist Competitive algorithm and half sphere exposure prediction. J Theor Biol 391:81–87CrossRefGoogle Scholar
  21. Khimasia MM, Coveney PV (1997) Protein structure prediction as a hard optimization problem: the genetic algorithm approach. Mol Simul 19(4):205–226CrossRefGoogle Scholar
  22. Larranaga P (2002) A review on estimation of distribution algorithms. Estimation of distribution algorithms. Springer, New York, pp 57–100CrossRefGoogle Scholar
  23. Lee SY, Lee JY, Jung KS, Ryu KH (2009) A 9-state hidden Markov model using protein secondary structure information for protein fold recognition. Comput Biol Med 39(6):527–534CrossRefGoogle Scholar
  24. Liu J, Sun Y, Li G, Song B, Huang W (2013) Heuristic-based tabu search algorithm for folding two-dimensional AB off-lattice model proteins. Comput Biol Chem 47:142–148CrossRefGoogle Scholar
  25. Mansour N, Kanj F, Khachfe H (2010) Evolutionary algorithm for protein structure prediction. In: 2010 sixth international conference on natural computation (ICNC), vol 8. IEEE, pp 3974–3977Google Scholar
  26. Patton AL, Punch III WF, Goodman ED (1995) A standard GA approach to native protein conformation prediction. In: ICGA, pp 574–581Google Scholar
  27. Raman S, Huang YJ, Mao B, Rossi P, Aramini JM, Liu G, Montelione GT, Baker D (2010) Accurate automated protein NMR structure determination using unassigned NOESY data. J Am Chem Soc 132(1):202–207CrossRefGoogle Scholar
  28. Ramezani F, Lotfi S (2013) Social-based algorithm (SBA). Appl Soft Comput 13(5):2837–2856CrossRefGoogle Scholar
  29. Razmara J, Deris SB, Parvizpour S (2013) A rapid protein structure alignment algorithm based on a text modeling technique. Bioinformation 6(9):344CrossRefGoogle Scholar
  30. Santos J, Diéguez M (2011) Differential evolution for protein structure prediction using the HP model. International work-conference on the interplay between natural and artificial computation. Springer, Berlin, pp 323–333Google Scholar
  31. Shen HB, Yang J, Liu XJ, Chou KC (2005) Using supervised fuzzy clustering to predict protein structural classes. Biochem Biophys Res Commun 334(2):577–581CrossRefGoogle Scholar
  32. Shen Y, Vernon R, Baker D, Bax A (2009) De novo protein structure generation from incomplete chemical shift assignments. J Biomol NMR 43(2):63–78CrossRefGoogle Scholar
  33. Shmygelska A, Hoos HH (2005) An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem. BMC Bioinform 6(1):30CrossRefGoogle Scholar
  34. Spencer M, Eickholt J, Cheng J (2015) A deep learning network approach to ab initio protein secondary structure prediction. IEEE/ACM Trans Comput Biol Bioinf 12(1):103–112CrossRefGoogle Scholar
  35. Storm CN, Lyngsø RB (1999) Protein folding in the 2D HP model. Tech rep, Technical Report RS-99-16 BRICS, University of Aarhus, DenmarkGoogle Scholar
  36. Sudha S, Baskar S, Amali SMJ, Krishnaswamy S (2015) Protein structure prediction using diversity controlled self-adaptive differential evolution with local search. Soft Comput 19(6):1635–1646CrossRefGoogle Scholar
  37. Toma L, Toma S (1996) Contact interactions method: a new algorithm for protein folding simulations. Protein Sci 5(1):147–153CrossRefGoogle Scholar
  38. Unger R, Moult J (1993) Genetic algorithms for protein folding simulations. J Mol Biol 231(1):75–81CrossRefGoogle Scholar
  39. Wang Y, Mao H, Yi Z (2017) Protein secondary structure prediction by using deep learning method. Knowl-Based Syst 118:115–123CrossRefGoogle Scholar
  40. Xie S, Li Z, Hu H (2018) Protein secondary structure prediction based on the fuzzy support vector machine with the hyperplane optimization. Gene 642:74–83CrossRefGoogle Scholar
  41. Yue K, Fiebig KM, Thomas PD, Chan HS, Shakhnovich EI, Dill KA (1995) A test of lattice protein folding algorithms. Proc Nat Acad Sci 92(1):325–329CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Amir Morshedian
    • 1
  • Jafar Razmara
    • 1
  • Shahriar Lotfi
    • 1
  1. 1.Department of Computer Science, Faculty of Mathematical SciencesUniversity of TabrizTabrizIran

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